Today, technologies for object detection or recognition which have been developed and embedded in a variety of electronic devices are used. For example, pedestrian detection technology for preventing risks of pedestrians by detecting the pedestrians is being put to use in a variety of electronic devices.
The object detection or recognition technology includes a process of learning (i.e., classifying) acquired images. The performance of a detector (i.e., classifier) depends on which types of features showing positions and intensity values of pixels of images to learn will be used and how many images will be used for learning. Recently, as image database where a lot of image data are accumulated has been built, a lot of image data enough to be used for learning have come to be obtained easily.
In general, a classifier may learn negative images which do not include a target object to detect or recognize. A detection system may repeat a bootstrapping process under which hard negative images, even as negative images, mistaken for including the target object are collected and re-learned.
If the bootstrapping process is repeated by using a lot of negative images, the operation quantity of the bootstrapping process, however, increases too much, resulting in too much time for whole learning. For effective learning by the classifier, it is necessary to select and learn only hard negative images mistaken for positive images, i.e., images including the target object, although the hard negative images do not include the target object.